def _write_dicom_file(np_slice: np.ndarray, header: pydicom.FileDataset,
file_path: str):
"""Replace data in header with 2D numpy array and write to `file_path`.
Args:
np_slice (np.ndarray): 2D slice to encode in dicom file.
header (pydicom.FileDataset): DICOM header.
file_path: File path to write to.
"""
# Deep copy required in case headers are shared.
header = copy.deepcopy(header)
expected_dimensions = header.Rows, header.Columns
assert (
np_slice.shape == expected_dimensions
), "In-plane dimension mismatch - expected shape {}, got {}".format(
str(expected_dimensions), str(np_slice.shape))
np_slice_bytes = np_slice.tobytes()
bit_depth = int(
len(np_slice_bytes) / (np_slice.shape[0] * np_slice.shape[1]) * 8)
if bit_depth != header.BitsAllocated:
np_slice = _update_np_dtype(np_slice, header.BitsAllocated)
np_slice_bytes = np_slice.tobytes()
bit_depth = int(
len(np_slice_bytes) / (np_slice.shape[0] * np_slice.shape[1]) * 8)
assert bit_depth == header.BitsAllocated, "Bit depth mismatch: Expected {:d} got {:d}".format(
header.BitsAllocated, bit_depth)
header.PixelData = np_slice_bytes
header.save_as(file_path)
def __write_dicom_file__(self, np_slice: np.ndarray,
header: pydicom.FileDataset, filepath: str):
"""
Replace data in header with 2D numpy array and write to filepath
:param np_slice: a 2D numpy array
:param header: a pydicom.FileDataset with fields populated
:param filepath: Filepath to write dicom to
"""
expected_dimensions = header.Rows, header.Columns
assert np_slice.shape == expected_dimensions, "In-plane dimension mismatch - expected shape %s, got %s" % (
str(expected_dimensions), str(np_slice.shape))
np_slice_bytes = np_slice.tobytes()
bit_depth = int(
len(np_slice_bytes) / (np_slice.shape[0] * np_slice.shape[1]) * 8)
if bit_depth != header.BitsAllocated:
np_slice = __update_np_dtype__(np_slice, header.BitsAllocated)
np_slice_bytes = np_slice.tobytes()
bit_depth = int(
len(np_slice_bytes) / (np_slice.shape[0] * np_slice.shape[1]) *
8)
assert bit_depth == header.BitsAllocated, "Bit depth mismatch: Expected %d got %d" % (
header.BitsAllocated, bit_depth)
header.PixelData = np_slice_bytes
header.save_as(filepath)
def create_dcm_file(self):
suffix = '.dcm'
filename_little_endian = tempfile.NamedTemporaryFile(
suffix=suffix).name
filename_big_endian = tempfile.NamedTemporaryFile(suffix=suffix).name
print("Setting file meta information...")
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = '1.2.840.10008.5.1.4.1.1.2'
file_meta.MediaStorageSOPInstanceUID = "1.2.3"
file_meta.ImplementationClassUID = "1.2.3.4"
print("Setting dataset values...")
ds = FileDataset(filename_little_endian, {},
file_meta=file_meta,
preamble=b"\0" * 128)
ds.PatientName = self.get_patient_name(
) + " " + self.get_patient_surname()
ds.PatientID = self.get_patient_id()
ds.PatientSex = self.get_patient_sex()
ds.PatientAge = self.get_patient_age()
ds.PatientWeight = self.get_patient_weight()
ds.ImageComment = self.get_patient_comment()
ds.PatientBirthDate = self.get_patient_birth()
# Set the transfer syntax
ds.is_little_endian = True
ds.is_implicit_VR = True
# Set creation date/time
dt = datetime.datetime.now()
ds.ContentDate = dt.strftime('%Y%m%d')
timeStr = dt.strftime('%H%M%S.%f') # long format with micro seconds
ds.ContentTime = timeStr
ds.BitsAllocated = 16
ds.Rows = self.image.shape[0]
ds.Columns = self.image.shape[1]
ds.PixelRepresentation = 0
ds.SamplesPerPixel = 1
ds.PhotometricInterpretation = "MONOCHROME2"
image = self.image
image *= 255
image = image.astype("uint16")
ds.PixelData = Image.fromarray(image).tobytes()
print("Writing test file", filename_little_endian)
ds.save_as(filename_little_endian)
print("File saved.")
ds.file_meta.TransferSyntaxUID = pydicom.uid.ExplicitVRBigEndian
ds.is_little_endian = False
ds.is_implicit_VR = False
print("Writing test file as Big Endian Explicit VR",
filename_big_endian)
ds.save_as(filename_big_endian)
return ds
def generate_dicom_scans(dst, num_scans=10, intercept=0, slope=1):
spacing = (0.4 + 0.4 * np.random.rand(num_scans, 3) +
np.array([1 + 0.5 * np.random.rand(), 0, 0]))
origin = np.random.randint(-200, 200, (num_scans, 3))
for i in range(num_scans):
num_slices = np.random.randint(128, 169)
scan_id = np.random.randint(2**16)
scan_data = np.random.randint(0, 256, (num_slices, 128, 128))
folder = os.path.join(dst,
hex(scan_id).replace('x', '').upper().zfill(8))
if not os.path.exists(folder):
os.makedirs(folder)
for k in range(num_slices):
slice_name = (hex(scan_id + k).replace('x', '').upper().zfill(8))
filename = os.path.join(folder, slice_name)
pixel_array = (scan_data[k, ...] - intercept) / slope
locZ = float(origin[i, 0] + spacing[i, 0] * k)
locY, locX = float(origin[i, 1]), float(origin[i, 2])
file_meta = DicomDataset()
file_meta.MediaStorageSOPClassUID = "Secondary Capture Image Storage"
file_meta.MediaStorateSOPInstanceUID = (hex(scan_id).replace(
'x', '').upper().zfill(8))
file_meta.ImplementationClassUID = slice_name
dataset = DicomFileDataset(filename, {},
file_meta=file_meta,
preamble=b"\0" * 128)
dataset.PixelData = pixel_array.astype(np.uint16).tostring()
dataset.RescaleSlope = slope
dataset.RescaleIntercept = intercept
dataset.ImagePositionPatient = MultiValue(
type_constructor=float, iterable=[locZ, locY, locX])
dataset.PixelSpacing = MultiValue(
type_constructor=float,
iterable=[float(spacing[i, 1]),
float(spacing[i, 2])])
dataset.SliceThickness = float(spacing[i, 0])
dataset.Modality = 'WSD'
dataset.Columns = pixel_array.shape[0]
dataset.Rows = pixel_array.shape[1]
dataset.file_meta.TransferSyntaxUID = pydicom.uid.ImplicitVRLittleEndian
dataset.PixelRepresentation = 1
dataset.BitsAllocated = 16
dataset.BitsStored = 16
dataset.SamplesPerPixel = 1
write_file(filename, dataset)
def dump_dicom(data, folder, spacing=(1, 1, 1),
origin=(0, 0, 0), intercept=0, slope=1):
""" Dump 3D scan in dicom format.
Parameters
----------
data : ndarray
3D numpy array containing ct scan's data.
folder : str
folder where dicom files will be dumped.
spacing : ArrayLike
ndarray of shape (3,) that contains spacing along z, y, x axes.
origin : ArrayLike
ndarray of shape (3,) that contains origin for z, y, x axes.
interception : float
interception value. Default is 0.
slope : float
slope value. Default is 1.
"""
spacing = np.array(spacing).reshape(-1)
origin = np.array(origin).reshape(-1)
if not os.path.exists(folder):
os.makedirs(folder)
num_slices = data.shape[0]
scan_id = np.random.randint(2 ** 16)
for i in range(num_slices):
slice_name = (
hex(scan_id + i)
.replace('x', '')
.upper()
.zfill(8)
)
filename = os.path.join(folder, slice_name)
pixel_array = (data[i, ...] - intercept) / slope
locZ, locY, locX = (float(origin[0] + spacing[0] * i),
float(origin[1]), float(origin[2]))
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = 'Secondary Capture Image Storage'
file_meta.MediaStorageSOPInstanceUID = (
hex(scan_id)
.replace('x', '')
.upper()
.zfill(8)
)
file_meta.ImplementationClassUID = slice_name
dataset = FileDataset(filename, {},
file_meta=file_meta,
preamble=b"\0"*128)
dataset.PixelData = pixel_array.astype(np.uint16).tostring()
dataset.RescaleSlope = slope
dataset.RescaleIntercept = intercept
dataset.ImagePositionPatient = MultiValue(type_constructor=float,
iterable=[locZ, locY, locX])
dataset.PixelSpacing = MultiValue(type_constructor=float,
iterable=[float(spacing[1]),
float(spacing[2])])
dataset.SliceThickness = float(spacing[0])
dataset.Modality = 'WSD'
dataset.Columns = pixel_array.shape[0]
dataset.Rows = pixel_array.shape[1]
dataset.file_meta.TransferSyntaxUID = pydicom.uid.ImplicitVRLittleEndian
dataset.PixelRepresentation = 1
dataset.BitsAllocated = 16
dataset.BitsStored = 16
dataset.SamplesPerPixel = 1
write_file(filename, dataset)
def generate_common_dicom_dataset_series(self, slice_count: int,
system: Modality) -> list:
output_dataset = []
slice_pos = 0
slice_thickness = 0
study_uid = generate_uid()
series_uid = generate_uid()
frame_of_ref_uid = generate_uid()
date_ = datetime.now().date()
age = timedelta(days=45 * 365)
time_ = datetime.now().time()
cols = 2
rows = 2
bytes_per_voxel = 2
for i in range(0, slice_count):
file_meta = Dataset()
pixel_array = b"\0" * cols * rows * bytes_per_voxel
file_meta.MediaStorageSOPClassUID = sop_classes[system][1]
file_meta.MediaStorageSOPInstanceUID = generate_uid()
file_meta.ImplementationClassUID = generate_uid()
tmp_dataset = FileDataset('', {},
file_meta=file_meta,
preamble=pixel_array)
tmp_dataset.file_meta.TransferSyntaxUID = "1.2.840.10008.1.2.1"
tmp_dataset.SliceLocation = slice_pos + i * slice_thickness
tmp_dataset.SliceThickness = slice_thickness
tmp_dataset.WindowCenter = 1
tmp_dataset.WindowWidth = 2
tmp_dataset.AcquisitionNumber = 1
tmp_dataset.InstanceNumber = i
tmp_dataset.SeriesNumber = 1
tmp_dataset.ImageOrientationPatient = [
1.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000
]
tmp_dataset.ImagePositionPatient = [
0.0, 0.0, tmp_dataset.SliceLocation
]
tmp_dataset.ImageType = ['ORIGINAL', 'PRIMARY', 'AXIAL']
tmp_dataset.PixelSpacing = [1, 1]
tmp_dataset.PatientName = 'John Doe'
tmp_dataset.FrameOfReferenceUID = frame_of_ref_uid
tmp_dataset.SOPClassUID = sop_classes[system][1]
tmp_dataset.SOPInstanceUID = generate_uid()
tmp_dataset.SeriesInstanceUID = series_uid
tmp_dataset.StudyInstanceUID = study_uid
tmp_dataset.BitsAllocated = bytes_per_voxel * 8
tmp_dataset.BitsStored = bytes_per_voxel * 8
tmp_dataset.HighBit = (bytes_per_voxel * 8 - 1)
tmp_dataset.PixelRepresentation = 1
tmp_dataset.Columns = cols
tmp_dataset.Rows = rows
tmp_dataset.SamplesPerPixel = 1
tmp_dataset.AccessionNumber = '2'
tmp_dataset.AcquisitionDate = date_
tmp_dataset.AcquisitionTime = datetime.now().time()
tmp_dataset.AdditionalPatientHistory = 'UTERINE CA PRE-OP EVAL'
tmp_dataset.ContentDate = date_
tmp_dataset.ContentTime = datetime.now().time()
tmp_dataset.Manufacturer = 'Mnufacturer'
tmp_dataset.ManufacturerModelName = 'Model'
tmp_dataset.Modality = sop_classes[system][0]
tmp_dataset.PatientAge = '064Y'
tmp_dataset.PatientBirthDate = date_ - age
tmp_dataset.PatientID = 'ID0001'
tmp_dataset.PatientIdentityRemoved = 'YES'
tmp_dataset.PatientPosition = 'FFS'
tmp_dataset.PatientSex = 'F'
tmp_dataset.PhotometricInterpretation = 'MONOCHROME2'
tmp_dataset.PixelData = pixel_array
tmp_dataset.PositionReferenceIndicator = 'XY'
tmp_dataset.ProtocolName = 'some protocole'
tmp_dataset.ReferringPhysicianName = ''
tmp_dataset.SeriesDate = date_
tmp_dataset.SeriesDescription = 'test series '
tmp_dataset.SeriesTime = time_
tmp_dataset.SoftwareVersions = '01'
tmp_dataset.SpecificCharacterSet = 'ISO_IR 100'
tmp_dataset.StudyDate = date_
tmp_dataset.StudyDescription = 'test study'
tmp_dataset.StudyID = ''
if (system == Modality.CT):
tmp_dataset.RescaleIntercept = 0
tmp_dataset.RescaleSlope = 1
tmp_dataset.StudyTime = time_
output_dataset.append(tmp_dataset)
return output_dataset